Overview

Dataset statistics

Number of variables34
Number of observations353035
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory91.6 MiB
Average record size in memory272.0 B

Variable types

Numeric26
Categorical8

Alerts

tripduration is highly overall correlated with distHigh correlation
dist is highly overall correlated with end_lat and 1 other fieldsHigh correlation
birthyear is highly overall correlated with usertype and 2 other fieldsHigh correlation
years_old is highly overall correlated with usertype and 2 other fieldsHigh correlation
tempmax is highly overall correlated with month and 12 other fieldsHigh correlation
tempmin is highly overall correlated with month and 10 other fieldsHigh correlation
temp is highly overall correlated with month and 12 other fieldsHigh correlation
feelslike is highly overall correlated with month and 12 other fieldsHigh correlation
precip is highly overall correlated with visibility and 1 other fieldsHigh correlation
dew is highly overall correlated with month and 11 other fieldsHigh correlation
humidity is highly overall correlated with tempmax and 9 other fieldsHigh correlation
snow is highly overall correlated with snowdepth and 2 other fieldsHigh correlation
snowdepth is highly overall correlated with tempmax and 6 other fieldsHigh correlation
visibility is highly overall correlated with precip and 5 other fieldsHigh correlation
solarradiation is highly overall correlated with month and 12 other fieldsHigh correlation
cloudcover is highly overall correlated with tempmax and 8 other fieldsHigh correlation
usertype is highly overall correlated with birthyear and 1 other fieldsHigh correlation
gender is highly overall correlated with birthyear and 1 other fieldsHigh correlation
month is highly overall correlated with tempmax and 8 other fieldsHigh correlation
conditions is highly overall correlated with tempmax and 8 other fieldsHigh correlation
description is highly overall correlated with month and 15 other fieldsHigh correlation
seasons is highly overall correlated with month and 7 other fieldsHigh correlation
start_lon is highly overall correlated with start_station_id and 2 other fieldsHigh correlation
end_lon is highly overall correlated with start_lon and 3 other fieldsHigh correlation
start_station_id is highly overall correlated with start_lat and 1 other fieldsHigh correlation
start_lat is highly overall correlated with start_station_id and 2 other fieldsHigh correlation
end_station_id is highly overall correlated with end_lat and 1 other fieldsHigh correlation
end_lat is highly overall correlated with start_lat and 3 other fieldsHigh correlation
windspeed is highly overall correlated with month and 7 other fieldsHigh correlation
min has 5390 (1.5%) zerosZeros
tempmin has 4161 (1.2%) zerosZeros
precip has 200641 (56.8%) zerosZeros
snow has 345872 (98.0%) zerosZeros
snowdepth has 336604 (95.3%) zerosZeros

Reproduction

Analysis started2023-02-17 11:41:36.929010
Analysis finished2023-02-17 11:48:42.839580
Duration7 minutes and 5.91 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

tripduration
Real number (ℝ)

Distinct5749
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean525.93321
Minimum61
Maximum14395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:43.133583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile131
Q1228
median334
Q3548
95-th percentile1507
Maximum14395
Range14334
Interquartile range (IQR)320

Descriptive statistics

Standard deviation731.10143
Coefficient of variation (CV)1.3901032
Kurtosis90.39266
Mean525.93321
Median Absolute Deviation (MAD)133
Skewness7.644811
Sum1.8567283 × 108
Variance534509.31
MonotonicityNot monotonic
2023-02-17T12:48:43.420252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
244 1014
 
0.3%
246 998
 
0.3%
260 984
 
0.3%
233 978
 
0.3%
242 976
 
0.3%
243 971
 
0.3%
266 960
 
0.3%
250 956
 
0.3%
247 956
 
0.3%
263 953
 
0.3%
Other values (5739) 343289
97.2%
ValueCountFrequency (%)
61 72
< 0.1%
62 72
< 0.1%
63 93
< 0.1%
64 82
< 0.1%
65 80
< 0.1%
66 96
< 0.1%
67 112
< 0.1%
68 90
< 0.1%
69 119
< 0.1%
70 110
< 0.1%
ValueCountFrequency (%)
14395 2
< 0.1%
14380 1
< 0.1%
14367 1
< 0.1%
14362 1
< 0.1%
14350 1
< 0.1%
14345 1
< 0.1%
14338 1
< 0.1%
14304 1
< 0.1%
14301 1
< 0.1%
14291 1
< 0.1%

start_station_id
Real number (ℝ)

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3265.0056
Minimum3183
Maximum3694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:43.721169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3183
5-th percentile3183
Q13192
median3205
Q33272
95-th percentile3639
Maximum3694
Range511
Interquartile range (IQR)80

Descriptive statistics

Standard deviation138.43089
Coefficient of variation (CV)0.042398363
Kurtosis3.3999331
Mean3265.0056
Median Absolute Deviation (MAD)19
Skewness2.2057689
Sum1.1526612 × 109
Variance19163.112
MonotonicityNot monotonic
2023-02-17T12:48:43.970030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3186 40830
 
11.6%
3203 20842
 
5.9%
3183 18936
 
5.4%
3195 18149
 
5.1%
3202 15194
 
4.3%
3639 12088
 
3.4%
3267 10515
 
3.0%
3276 10330
 
2.9%
3199 10055
 
2.8%
3211 9437
 
2.7%
Other values (49) 186659
52.9%
ValueCountFrequency (%)
3183 18936
5.4%
3184 8696
 
2.5%
3185 8804
 
2.5%
3186 40830
11.6%
3187 9245
 
2.6%
3188 49
 
< 0.1%
3189 42
 
< 0.1%
3190 153
 
< 0.1%
3191 1015
 
0.3%
3192 7040
 
2.0%
ValueCountFrequency (%)
3694 390
 
0.1%
3681 4238
 
1.2%
3679 2411
 
0.7%
3678 2523
 
0.7%
3677 1157
 
0.3%
3640 5810
1.6%
3639 12088
3.4%
3638 7323
2.1%
3483 2617
 
0.7%
3481 3296
 
0.9%

start_lat
Real number (ℝ)

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.722726
Minimum40.69264
Maximum40.748716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:44.180303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum40.69264
5-th percentile40.712419
Q140.718211
median40.721525
Q340.727224
95-th percentile40.737604
Maximum40.748716
Range0.056075979
Interquartile range (IQR)0.0090122

Descriptive statistics

Standard deviation0.007249213
Coefficient of variation (CV)0.00017801394
Kurtosis1.3678635
Mean40.722726
Median Absolute Deviation (MAD)0.0044865796
Skewness0.97789544
Sum14376548
Variance5.2551089 × 10-5
MonotonicityNot monotonic
2023-02-17T12:48:44.402721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71958612 40830
 
11.6%
40.72759597 20842
 
5.9%
40.7162469 18936
 
5.4%
40.73074263 18149
 
5.1%
40.7272235 15194
 
4.3%
40.7192517 12088
 
3.4%
40.71241882 10515
 
3.0%
40.71458404 10330
 
2.9%
40.7287448 10055
 
2.8%
40.72152515 9437
 
2.7%
Other values (49) 186659
52.9%
ValueCountFrequency (%)
40.69263997 17
 
< 0.1%
40.6970299 14
 
< 0.1%
40.69865054 34
 
< 0.1%
40.70495752 23
 
< 0.1%
40.70965083 52
 
< 0.1%
40.7101087 49
 
< 0.1%
40.71046702 153
 
< 0.1%
40.71113 390
 
0.1%
40.7111305 46
 
< 0.1%
40.7112423 7040
2.0%
ValueCountFrequency (%)
40.74871595 2556
0.7%
40.74590997 2676
0.8%
40.7443187 2066
 
0.6%
40.74267714 4420
1.3%
40.737711 1167
 
0.3%
40.7376037 5377
1.5%
40.73496102 2024
 
0.6%
40.73478582 2407
0.7%
40.73367 5810
1.6%
40.7311689 3067
0.9%

start_lon
Real number (ℝ)

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-74.046039
Minimum-74.096937
Maximum-74.032108
Zeros0
Zeros (%)0.0%
Negative353035
Negative (%)100.0%
Memory size2.7 MiB
2023-02-17T12:48:44.691720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-74.096937
5-th percentile-74.067622
Q1-74.050444
median-74.043845
Q3-74.038051
95-th percentile-74.033459
Maximum-74.032108
Range0.0648284
Interquartile range (IQR)0.012392686

Descriptive statistics

Standard deviation0.010753324
Coefficient of variation (CV)-0.00014522484
Kurtosis0.47010856
Mean-74.046039
Median Absolute Deviation (MAD)0.0061616919
Skewness-0.96456733
Sum-26140844
Variance0.00011563399
MonotonicityNot monotonic
2023-02-17T12:48:44.930244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.04311746 40830
 
11.6%
-74.04424731 20842
 
5.9%
-74.0334588 18936
 
5.4%
-74.06378388 18149
 
5.1%
-74.0337589 15194
 
4.3%
-74.034234 12088
 
3.4%
-74.03852552 10515
 
3.0%
-74.04281706 10330
 
2.9%
-74.0321082 10055
 
2.8%
-74.04630454 9437
 
2.7%
Other values (49) 186659
52.9%
ValueCountFrequency (%)
-74.0969366 14
 
< 0.1%
-74.0887723 42
 
< 0.1%
-74.08801228 17
 
< 0.1%
-74.08593088 23
 
< 0.1%
-74.0858489 49
 
< 0.1%
-74.0836394 1015
 
0.3%
-74.08207968 34
 
< 0.1%
-74.0789 390
 
0.1%
-74.0788855 46
 
< 0.1%
-74.07840595 2565
0.7%
ValueCountFrequency (%)
-74.0321082 10055
2.8%
-74.0334588 18936
5.4%
-74.0335519 8696
2.5%
-74.0337589 15194
4.3%
-74.034234 12088
3.4%
-74.0354826 7323
 
2.1%
-74.0364857 6423
 
1.8%
-74.03768331 4238
 
1.2%
-74.03805095 9245
2.6%
-74.03852552 10515
3.0%

end_station_id
Real number (ℝ)

Distinct121
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3258.3976
Minimum127
Maximum3694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:45.224375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum127
5-th percentile3183
Q13186
median3203
Q33270
95-th percentile3639
Maximum3694
Range3567
Interquartile range (IQR)84

Descriptive statistics

Standard deviation147.20129
Coefficient of variation (CV)0.045175974
Kurtosis64.396528
Mean3258.3976
Median Absolute Deviation (MAD)17
Skewness-1.3715088
Sum1.1503284 × 109
Variance21668.219
MonotonicityNot monotonic
2023-02-17T12:48:45.426218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3186 50536
 
14.3%
3183 24065
 
6.8%
3203 19635
 
5.6%
3202 16286
 
4.6%
3195 16283
 
4.6%
3639 11620
 
3.3%
3276 10055
 
2.8%
3199 9994
 
2.8%
3211 9574
 
2.7%
3185 9438
 
2.7%
Other values (111) 175549
49.7%
ValueCountFrequency (%)
127 1
 
< 0.1%
146 3
 
< 0.1%
157 9
< 0.1%
167 1
 
< 0.1%
212 1
 
< 0.1%
254 1
 
< 0.1%
259 1
 
< 0.1%
264 1
 
< 0.1%
276 1
 
< 0.1%
303 2
 
< 0.1%
ValueCountFrequency (%)
3694 333
 
0.1%
3681 4046
 
1.1%
3679 2100
 
0.6%
3678 1915
 
0.5%
3677 814
 
0.2%
3640 5287
1.5%
3639 11620
3.3%
3638 7348
2.1%
3552 1
 
< 0.1%
3547 3
 
< 0.1%

end_lat
Real number (ℝ)

Distinct121
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.722327
Minimum40.679331
Maximum40.814326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:45.869883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum40.679331
5-th percentile40.712774
Q140.717732
median40.721124
Q340.727224
95-th percentile40.734961
Maximum40.814326
Range0.1349949
Interquartile range (IQR)0.009491

Descriptive statistics

Standard deviation0.0070856291
Coefficient of variation (CV)0.00017399863
Kurtosis2.1663154
Mean40.722327
Median Absolute Deviation (MAD)0.0046025374
Skewness1.1086587
Sum14376407
Variance5.020614 × 10-5
MonotonicityNot monotonic
2023-02-17T12:48:46.126683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71958612 50536
 
14.3%
40.7162469 24065
 
6.8%
40.72759597 19635
 
5.6%
40.7272235 16286
 
4.6%
40.73074263 16283
 
4.6%
40.7192517 11620
 
3.3%
40.71458404 10055
 
2.8%
40.7287448 9994
 
2.8%
40.72152515 9574
 
2.7%
40.7177325 9438
 
2.7%
Other values (111) 175549
49.7%
ValueCountFrequency (%)
40.6793307 1
 
< 0.1%
40.68539567 1
 
< 0.1%
40.68763155 1
 
< 0.1%
40.69089272 9
 
< 0.1%
40.69165183 6
 
< 0.1%
40.69263997 15
< 0.1%
40.6970299 22
< 0.1%
40.69865054 31
< 0.1%
40.70122128 1
 
< 0.1%
40.701907 2
 
< 0.1%
ValueCountFrequency (%)
40.8143256 3
< 0.1%
40.8067581 1
 
< 0.1%
40.805973 1
 
< 0.1%
40.7961535 1
 
< 0.1%
40.7746671 1
 
< 0.1%
40.7734066 2
< 0.1%
40.770513 1
 
< 0.1%
40.768254 1
 
< 0.1%
40.76590936 1
 
< 0.1%
40.76370739 4
< 0.1%

end_lon
Real number (ℝ)

Distinct121
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-74.045504
Minimum-74.096937
Maximum-73.947821
Zeros0
Zeros (%)0.0%
Negative353035
Negative (%)100.0%
Memory size2.7 MiB
2023-02-17T12:48:46.381207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-74.096937
5-th percentile-74.066921
Q1-74.049968
median-74.043117
Q3-74.037683
95-th percentile-74.033459
Maximum-73.947821
Range0.14911515
Interquartile range (IQR)0.012284517

Descriptive statistics

Standard deviation0.010750348
Coefficient of variation (CV)-0.0001451857
Kurtosis1.0340059
Mean-74.045504
Median Absolute Deviation (MAD)0.0066317636
Skewness-1.0355317
Sum-26140654
Variance0.00011556998
MonotonicityNot monotonic
2023-02-17T12:48:46.706289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.04311746 50536
 
14.3%
-74.0334588 24065
 
6.8%
-74.04424731 19635
 
5.6%
-74.0337589 16286
 
4.6%
-74.06378388 16283
 
4.6%
-74.034234 11620
 
3.3%
-74.04281706 10055
 
2.8%
-74.0321082 9994
 
2.8%
-74.04630454 9574
 
2.7%
-74.043845 9438
 
2.7%
Other values (111) 175549
49.7%
ValueCountFrequency (%)
-74.0969366 22
 
< 0.1%
-74.0887723 96
 
< 0.1%
-74.08801228 15
 
< 0.1%
-74.08593088 23
 
< 0.1%
-74.0858489 50
 
< 0.1%
-74.0836394 1295
0.4%
-74.08207968 31
 
< 0.1%
-74.0789 333
 
0.1%
-74.0788855 32
 
< 0.1%
-74.07840595 3211
0.9%
ValueCountFrequency (%)
-73.94782145 1
 
< 0.1%
-73.9590255 3
< 0.1%
-73.9607082 1
 
< 0.1%
-73.964928 1
 
< 0.1%
-73.97031366 1
 
< 0.1%
-73.97121214 1
 
< 0.1%
-73.97431458 1
 
< 0.1%
-73.97498696 1
 
< 0.1%
-73.97519523 1
 
< 0.1%
-73.97604882 1
 
< 0.1%

bikeid
Real number (ℝ)

Distinct903
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29452.978
Minimum14697
Maximum35009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:46.954061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum14697
5-th percentile26183
Q126315
median29493
Q329679
95-th percentile33638
Maximum35009
Range20312
Interquartile range (IQR)3364

Descriptive statistics

Standard deviation2529.8244
Coefficient of variation (CV)0.085893671
Kurtosis0.83380671
Mean29452.978
Median Absolute Deviation (MAD)1911
Skewness-0.12760211
Sum1.0397932 × 1010
Variance6400011.7
MonotonicityNot monotonic
2023-02-17T12:48:47.156420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26155 872
 
0.2%
26288 852
 
0.2%
29586 840
 
0.2%
29598 832
 
0.2%
29608 831
 
0.2%
29595 824
 
0.2%
29669 813
 
0.2%
29583 810
 
0.2%
29602 804
 
0.2%
29662 794
 
0.2%
Other values (893) 344763
97.7%
ValueCountFrequency (%)
14697 11
 
< 0.1%
14793 18
 
< 0.1%
14956 5
 
< 0.1%
14977 49
< 0.1%
14991 43
 
< 0.1%
15114 47
 
< 0.1%
15271 25
 
< 0.1%
15302 118
< 0.1%
15444 15
 
< 0.1%
15582 12
 
< 0.1%
ValueCountFrequency (%)
35009 37
 
< 0.1%
34791 10
 
< 0.1%
34676 10
 
< 0.1%
34664 1
 
< 0.1%
34354 31
 
< 0.1%
34155 1
 
< 0.1%
33840 164
< 0.1%
33814 155
< 0.1%
33781 160
< 0.1%
33744 83
< 0.1%

usertype
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Subscriber
331269 
Customer
 
21766

Length

Max length10
Median length10
Mean length9.8766921
Min length8

Characters and Unicode

Total characters3486818
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSubscriber
2nd rowSubscriber
3rd rowSubscriber
4th rowSubscriber
5th rowSubscriber

Common Values

ValueCountFrequency (%)
Subscriber 331269
93.8%
Customer 21766
 
6.2%

Length

2023-02-17T12:48:47.379434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T12:48:47.645885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
subscriber 331269
93.8%
customer 21766
 
6.2%

Most occurring characters

ValueCountFrequency (%)
r 684304
19.6%
b 662538
19.0%
u 353035
10.1%
s 353035
10.1%
e 353035
10.1%
S 331269
9.5%
c 331269
9.5%
i 331269
9.5%
C 21766
 
0.6%
t 21766
 
0.6%
Other values (2) 43532
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3133783
89.9%
Uppercase Letter 353035
 
10.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 684304
21.8%
b 662538
21.1%
u 353035
11.3%
s 353035
11.3%
e 353035
11.3%
c 331269
10.6%
i 331269
10.6%
t 21766
 
0.7%
o 21766
 
0.7%
m 21766
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
S 331269
93.8%
C 21766
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 3486818
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 684304
19.6%
b 662538
19.0%
u 353035
10.1%
s 353035
10.1%
e 353035
10.1%
S 331269
9.5%
c 331269
9.5%
i 331269
9.5%
C 21766
 
0.6%
t 21766
 
0.6%
Other values (2) 43532
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3486818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 684304
19.6%
b 662538
19.0%
u 353035
10.1%
s 353035
10.1%
e 353035
10.1%
S 331269
9.5%
c 331269
9.5%
i 331269
9.5%
C 21766
 
0.6%
t 21766
 
0.6%
Other values (2) 43532
 
1.2%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
male
256901 
female
75171 
unknown
 
20963

Length

Max length7
Median length4
Mean length4.6039939
Min length4

Characters and Unicode

Total characters1625371
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
male 256901
72.8%
female 75171
 
21.3%
unknown 20963
 
5.9%

Length

2023-02-17T12:48:47.823316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T12:48:47.972101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
male 256901
72.8%
female 75171
 
21.3%
unknown 20963
 
5.9%

Most occurring characters

ValueCountFrequency (%)
e 407243
25.1%
m 332072
20.4%
a 332072
20.4%
l 332072
20.4%
f 75171
 
4.6%
n 62889
 
3.9%
u 20963
 
1.3%
k 20963
 
1.3%
o 20963
 
1.3%
w 20963
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1625371
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 407243
25.1%
m 332072
20.4%
a 332072
20.4%
l 332072
20.4%
f 75171
 
4.6%
n 62889
 
3.9%
u 20963
 
1.3%
k 20963
 
1.3%
o 20963
 
1.3%
w 20963
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1625371
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 407243
25.1%
m 332072
20.4%
a 332072
20.4%
l 332072
20.4%
f 75171
 
4.6%
n 62889
 
3.9%
u 20963
 
1.3%
k 20963
 
1.3%
o 20963
 
1.3%
w 20963
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1625371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 407243
25.1%
m 332072
20.4%
a 332072
20.4%
l 332072
20.4%
f 75171
 
4.6%
n 62889
 
3.9%
u 20963
 
1.3%
k 20963
 
1.3%
o 20963
 
1.3%
w 20963
 
1.3%

dist
Real number (ℝ)

Distinct2663
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.78422928
Minimum0
Maximum9.1122582
Zeros2325
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:48.171519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15866545
Q10.43349609
median0.65904833
Q30.96379915
95-th percentile1.8366588
Maximum9.1122582
Range9.1122582
Interquartile range (IQR)0.53030306

Descriptive statistics

Standard deviation0.542201
Coefficient of variation (CV)0.69138071
Kurtosis5.2340002
Mean0.78422928
Median Absolute Deviation (MAD)0.24335259
Skewness1.8033726
Sum276860.38
Variance0.29398192
MonotonicityNot monotonic
2023-02-17T12:48:48.378942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6590483332 7869
 
2.2%
0.6590574045 5870
 
1.7%
0.4156981062 4599
 
1.3%
0.7841604255 4263
 
1.2%
0.4396170481 4131
 
1.2%
0.7508857537 3984
 
1.1%
0.415695745 3318
 
0.9%
0.6185235707 3279
 
0.9%
0.4188632139 3200
 
0.9%
0.34202925 3176
 
0.9%
Other values (2653) 309346
87.6%
ValueCountFrequency (%)
0 2325
0.7%
9.818762165 × 10-131961
0.6%
1.488035366 × 10-1284
 
< 0.1%
1.488100836 × 10-12238
 
0.1%
1.488206632 × 10-1296
 
< 0.1%
1.488245041 × 10-12421
 
0.1%
1.488391755 × 10-12412
 
0.1%
1.488418421 × 10-12461
 
0.1%
1.488818307 × 10-123
 
< 0.1%
1.488952632 × 10-122
 
< 0.1%
ValueCountFrequency (%)
9.112258173 1
< 0.1%
9.041788085 1
< 0.1%
8.954820805 1
< 0.1%
8.727328843 1
< 0.1%
8.622532883 1
< 0.1%
8.568639995 1
< 0.1%
7.023637674 1
< 0.1%
6.04772788 1
< 0.1%
5.887380573 1
< 0.1%
5.840948824 1
< 0.1%

month
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
August
44325 
July
42171 
June
40820 
October
39044 
September
38919 
Other values (7)
147756 

Length

Max length9
Median length7
Mean length5.9405696
Min length3

Characters and Unicode

Total characters2097229
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowJanuary
3rd rowJanuary
4th rowJanuary
5th rowJanuary

Common Values

ValueCountFrequency (%)
August 44325
12.6%
July 42171
11.9%
June 40820
11.6%
October 39044
11.1%
September 38919
11.0%
May 34352
9.7%
November 24876
7.0%
April 23574
6.7%
December 20182
5.7%
March 17064
 
4.8%
Other values (2) 27708
7.8%

Length

2023-02-17T12:48:48.593215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 44325
12.6%
july 42171
11.9%
june 40820
11.6%
october 39044
11.1%
september 38919
11.0%
may 34352
9.7%
november 24876
7.0%
april 23574
6.7%
december 20182
5.7%
march 17064
 
4.8%
Other values (2) 27708
7.8%

Most occurring characters

ValueCountFrequency (%)
e 321989
15.4%
r 206437
 
9.8%
u 199349
 
9.5%
b 138091
 
6.6%
t 122288
 
5.8%
y 104231
 
5.0%
J 95629
 
4.6%
a 91762
 
4.4%
m 83977
 
4.0%
c 76290
 
3.6%
Other values (16) 657186
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1744194
83.2%
Uppercase Letter 353035
 
16.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 321989
18.5%
r 206437
11.8%
u 199349
11.4%
b 138091
7.9%
t 122288
 
7.0%
y 104231
 
6.0%
a 91762
 
5.3%
m 83977
 
4.8%
c 76290
 
4.4%
l 65745
 
3.8%
Other values (8) 334035
19.2%
Uppercase Letter
ValueCountFrequency (%)
J 95629
27.1%
A 67899
19.2%
M 51416
14.6%
O 39044
11.1%
S 38919
11.0%
N 24876
 
7.0%
D 20182
 
5.7%
F 15070
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2097229
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 321989
15.4%
r 206437
 
9.8%
u 199349
 
9.5%
b 138091
 
6.6%
t 122288
 
5.8%
y 104231
 
5.0%
J 95629
 
4.6%
a 91762
 
4.4%
m 83977
 
4.0%
c 76290
 
3.6%
Other values (16) 657186
31.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2097229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 321989
15.4%
r 206437
 
9.8%
u 199349
 
9.5%
b 138091
 
6.6%
t 122288
 
5.8%
y 104231
 
5.0%
J 95629
 
4.6%
a 91762
 
4.4%
m 83977
 
4.0%
c 76290
 
3.6%
Other values (16) 657186
31.3%

day
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
weekday
278318 
weekend
74717 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters2471245
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowweekday
2nd rowweekday
3rd rowweekday
4th rowweekday
5th rowweekday

Common Values

ValueCountFrequency (%)
weekday 278318
78.8%
weekend 74717
 
21.2%

Length

2023-02-17T12:48:48.833574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T12:48:49.025021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
weekday 278318
78.8%
weekend 74717
 
21.2%

Most occurring characters

ValueCountFrequency (%)
e 780787
31.6%
w 353035
14.3%
k 353035
14.3%
d 353035
14.3%
a 278318
 
11.3%
y 278318
 
11.3%
n 74717
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2471245
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 780787
31.6%
w 353035
14.3%
k 353035
14.3%
d 353035
14.3%
a 278318
 
11.3%
y 278318
 
11.3%
n 74717
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2471245
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 780787
31.6%
w 353035
14.3%
k 353035
14.3%
d 353035
14.3%
a 278318
 
11.3%
y 278318
 
11.3%
n 74717
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2471245
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 780787
31.6%
w 353035
14.3%
k 353035
14.3%
d 353035
14.3%
a 278318
 
11.3%
y 278318
 
11.3%
n 74717
 
3.0%

hour
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.633679
Minimum0
Maximum23
Zeros2750
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:49.166134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q19
median14
Q318
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.1565432
Coefficient of variation (CV)0.37822095
Kurtosis-1.0104156
Mean13.633679
Median Absolute Deviation (MAD)5
Skewness-0.20483033
Sum4813166
Variance26.589937
MonotonicityNot monotonic
2023-02-17T12:48:49.348640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8 42943
12.2%
18 37328
 
10.6%
17 32759
 
9.3%
19 26627
 
7.5%
7 24169
 
6.8%
9 21232
 
6.0%
20 18473
 
5.2%
16 18405
 
5.2%
12 15123
 
4.3%
15 15058
 
4.3%
Other values (14) 100918
28.6%
ValueCountFrequency (%)
0 2750
 
0.8%
1 1387
 
0.4%
2 744
 
0.2%
3 519
 
0.1%
4 786
 
0.2%
5 3531
 
1.0%
6 10156
 
2.9%
7 24169
6.8%
8 42943
12.2%
9 21232
6.0%
ValueCountFrequency (%)
23 4671
 
1.3%
22 8055
 
2.3%
21 12194
 
3.5%
20 18473
5.2%
19 26627
7.5%
18 37328
10.6%
17 32759
9.3%
16 18405
5.2%
15 15058
4.3%
14 14224
 
4.0%

min
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.544399
Minimum0
Maximum59
Zeros5390
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:49.557004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q115
median30
Q344
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.265944
Coefficient of variation (CV)0.58440666
Kurtosis-1.2065094
Mean29.544399
Median Absolute Deviation (MAD)15
Skewness-0.0037686619
Sum10430207
Variance298.11281
MonotonicityNot monotonic
2023-02-17T12:48:49.728149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44 6284
 
1.8%
23 6182
 
1.8%
24 6181
 
1.8%
43 6162
 
1.7%
25 6146
 
1.7%
53 6122
 
1.7%
45 6092
 
1.7%
41 6072
 
1.7%
11 6048
 
1.7%
22 6044
 
1.7%
Other values (50) 291702
82.6%
ValueCountFrequency (%)
0 5390
1.5%
1 5655
1.6%
2 5712
1.6%
3 5913
1.7%
4 5929
1.7%
5 5893
1.7%
6 6007
1.7%
7 5724
1.6%
8 5932
1.7%
9 5938
1.7%
ValueCountFrequency (%)
59 5559
1.6%
58 5558
1.6%
57 5811
1.6%
56 5944
1.7%
55 5801
1.6%
54 5860
1.7%
53 6122
1.7%
52 5880
1.7%
51 5967
1.7%
50 5990
1.7%

birthyear
Real number (ℝ)

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1980.4391
Minimum1939
Maximum2002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:49.960020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1939
5-th percentile1960
Q11974
median1983
Q31988
95-th percentile1993
Maximum2002
Range63
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.09409
Coefficient of variation (CV)0.005096895
Kurtosis-0.014329794
Mean1980.4391
Median Absolute Deviation (MAD)6
Skewness-0.80006489
Sum6.9916432 × 108
Variance101.89066
MonotonicityNot monotonic
2023-02-17T12:48:50.200035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1969 22215
 
6.3%
1988 21646
 
6.1%
1986 19023
 
5.4%
1989 17852
 
5.1%
1987 16566
 
4.7%
1990 15322
 
4.3%
1985 14916
 
4.2%
1983 14488
 
4.1%
1984 14392
 
4.1%
1991 13872
 
3.9%
Other values (54) 182743
51.8%
ValueCountFrequency (%)
1939 2
 
< 0.1%
1940 3
 
< 0.1%
1941 132
< 0.1%
1942 9
 
< 0.1%
1943 2
 
< 0.1%
1944 73
< 0.1%
1945 4
 
< 0.1%
1946 15
 
< 0.1%
1947 41
 
< 0.1%
1948 12
 
< 0.1%
ValueCountFrequency (%)
2002 17
 
< 0.1%
2001 22
 
< 0.1%
2000 198
 
0.1%
1999 162
 
< 0.1%
1998 519
 
0.1%
1997 434
 
0.1%
1996 1834
 
0.5%
1995 3654
1.0%
1994 7831
2.2%
1993 8314
2.4%

years_old
Real number (ℝ)

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.560888
Minimum16
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:50.704460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile25
Q130
median35
Q344
95-th percentile58
Maximum79
Range63
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.09409
Coefficient of variation (CV)0.26873939
Kurtosis-0.014329794
Mean37.560888
Median Absolute Deviation (MAD)6
Skewness0.80006489
Sum13260308
Variance101.89066
MonotonicityNot monotonic
2023-02-17T12:48:50.984500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 22215
 
6.3%
30 21646
 
6.1%
32 19023
 
5.4%
29 17852
 
5.1%
31 16566
 
4.7%
28 15322
 
4.3%
33 14916
 
4.2%
35 14488
 
4.1%
34 14392
 
4.1%
27 13872
 
3.9%
Other values (54) 182743
51.8%
ValueCountFrequency (%)
16 17
 
< 0.1%
17 22
 
< 0.1%
18 198
 
0.1%
19 162
 
< 0.1%
20 519
 
0.1%
21 434
 
0.1%
22 1834
 
0.5%
23 3654
1.0%
24 7831
2.2%
25 8314
2.4%
ValueCountFrequency (%)
79 2
 
< 0.1%
78 3
 
< 0.1%
77 132
< 0.1%
76 9
 
< 0.1%
75 2
 
< 0.1%
74 73
< 0.1%
73 4
 
< 0.1%
72 15
 
< 0.1%
71 41
 
< 0.1%
70 12
 
< 0.1%

holiday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
working_day
345584 
holiday
 
7451

Length

Max length11
Median length11
Mean length10.915578
Min length7

Characters and Unicode

Total characters3853581
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowholiday
2nd rowholiday
3rd rowholiday
4th rowholiday
5th rowholiday

Common Values

ValueCountFrequency (%)
working_day 345584
97.9%
holiday 7451
 
2.1%

Length

2023-02-17T12:48:51.268797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T12:48:51.442227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
working_day 345584
97.9%
holiday 7451
 
2.1%

Most occurring characters

ValueCountFrequency (%)
o 353035
9.2%
i 353035
9.2%
d 353035
9.2%
a 353035
9.2%
y 353035
9.2%
w 345584
9.0%
r 345584
9.0%
k 345584
9.0%
n 345584
9.0%
g 345584
9.0%
Other values (3) 360486
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3507997
91.0%
Connector Punctuation 345584
 
9.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 353035
10.1%
i 353035
10.1%
d 353035
10.1%
a 353035
10.1%
y 353035
10.1%
w 345584
9.9%
r 345584
9.9%
k 345584
9.9%
n 345584
9.9%
g 345584
9.9%
Other values (2) 14902
 
0.4%
Connector Punctuation
ValueCountFrequency (%)
_ 345584
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3507997
91.0%
Common 345584
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 353035
10.1%
i 353035
10.1%
d 353035
10.1%
a 353035
10.1%
y 353035
10.1%
w 345584
9.9%
r 345584
9.9%
k 345584
9.9%
n 345584
9.9%
g 345584
9.9%
Other values (2) 14902
 
0.4%
Common
ValueCountFrequency (%)
_ 345584
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3853581
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 353035
9.2%
i 353035
9.2%
d 353035
9.2%
a 353035
9.2%
y 353035
9.2%
w 345584
9.0%
r 345584
9.0%
k 345584
9.0%
n 345584
9.0%
g 345584
9.0%
Other values (3) 360486
9.4%

tempmax
Real number (ℝ)

Distinct116
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.393652
Minimum-11.1
Maximum35
Zeros311
Zeros (%)0.1%
Negative2581
Negative (%)0.7%
Memory size2.7 MiB
2023-02-17T12:48:51.671605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-11.1
5-th percentile3.9
Q112.9
median22.9
Q327.8
95-th percentile32.2
Maximum35
Range46.1
Interquartile range (IQR)14.9

Descriptive statistics

Standard deviation9.1360839
Coefficient of variation (CV)0.44798666
Kurtosis-0.83365281
Mean20.393652
Median Absolute Deviation (MAD)6.2
Skewness-0.53979696
Sum7199672.9
Variance83.46803
MonotonicityNot monotonic
2023-02-17T12:48:51.857101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.4 13699
 
3.9%
26.7 13189
 
3.7%
27.2 10677
 
3.0%
26.2 9536
 
2.7%
25.1 8658
 
2.5%
21.7 8106
 
2.3%
31.7 8078
 
2.3%
22.2 7624
 
2.2%
31.2 7382
 
2.1%
30.7 7273
 
2.1%
Other values (106) 258813
73.3%
ValueCountFrequency (%)
-11.1 75
 
< 0.1%
-7.8 191
0.1%
-7.1 120
 
< 0.1%
-4.4 191
0.1%
-3.3 391
0.1%
-2.8 175
 
< 0.1%
-1.7 52
 
< 0.1%
-1.6 467
0.1%
-1.2 296
0.1%
-1.1 301
0.1%
ValueCountFrequency (%)
35 2296
 
0.7%
33.9 1600
 
0.5%
33.4 4404
1.2%
33.3 1237
 
0.4%
32.9 4294
1.2%
32.2 4796
1.4%
31.7 8078
2.3%
31.2 7382
2.1%
31.1 1425
 
0.4%
30.7 7273
2.1%

tempmin
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct112
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.408749
Minimum-15
Maximum27.2
Zeros4161
Zeros (%)1.2%
Negative25652
Negative (%)7.3%
Memory size2.7 MiB
2023-02-17T12:48:52.088748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-15
5-th percentile-1.7
Q16.1
median15.7
Q320.7
95-th percentile24.4
Maximum27.2
Range42.2
Interquartile range (IQR)14.6

Descriptive statistics

Standard deviation8.7414052
Coefficient of variation (CV)0.65191801
Kurtosis-0.83557998
Mean13.408749
Median Absolute Deviation (MAD)6.5
Skewness-0.51587694
Sum4733757.8
Variance76.412165
MonotonicityNot monotonic
2023-02-17T12:48:52.339116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.7 17943
 
5.1%
21.7 14211
 
4.0%
19.4 12439
 
3.5%
22.9 11145
 
3.2%
20.1 9991
 
2.8%
18.9 9435
 
2.7%
24.4 9227
 
2.6%
23.4 9155
 
2.6%
17.2 8671
 
2.5%
22.2 8270
 
2.3%
Other values (102) 242548
68.7%
ValueCountFrequency (%)
-15 90
 
< 0.1%
-13.8 176
 
< 0.1%
-12.8 120
 
< 0.1%
-10.9 391
0.1%
-9.3 502
0.1%
-8.8 597
0.2%
-8.4 467
0.1%
-8.3 816
0.2%
-7.8 699
0.2%
-7.1 469
0.1%
ValueCountFrequency (%)
27.2 1593
 
0.5%
26.6 1442
 
0.4%
26.2 3167
 
0.9%
26.1 854
 
0.2%
25.7 2895
 
0.8%
25.1 3034
 
0.9%
25 2522
 
0.7%
24.4 9227
2.6%
23.9 1631
 
0.5%
23.4 9155
2.6%

temp
Real number (ℝ)

Distinct234
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.765239
Minimum-12.4
Maximum30.5
Zeros0
Zeros (%)0.0%
Negative9225
Negative (%)2.6%
Memory size2.7 MiB
2023-02-17T12:48:52.604564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-12.4
5-th percentile1.5
Q19.1
median19.5
Q323.5
95-th percentile27.8
Maximum30.5
Range42.9
Interquartile range (IQR)14.4

Descriptive statistics

Standard deviation8.8048127
Coefficient of variation (CV)0.52518267
Kurtosis-0.84871713
Mean16.765239
Median Absolute Deviation (MAD)6
Skewness-0.53961047
Sum5918716
Variance77.524727
MonotonicityNot monotonic
2023-02-17T12:48:52.863538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.4 9434
 
2.7%
17.1 6581
 
1.9%
23.2 5977
 
1.7%
20.8 5661
 
1.6%
27.7 4674
 
1.3%
27.8 4551
 
1.3%
23.3 4549
 
1.3%
23 4465
 
1.3%
23.1 4459
 
1.3%
24.6 4351
 
1.2%
Other values (224) 298333
84.5%
ValueCountFrequency (%)
-12.4 75
 
< 0.1%
-11.4 90
 
< 0.1%
-10.7 101
 
< 0.1%
-10.4 120
 
< 0.1%
-7.5 391
0.1%
-7.4 191
 
0.1%
-5.2 296
0.1%
-5 642
0.2%
-4.6 301
0.1%
-4.3 311
0.1%
ValueCountFrequency (%)
30.5 854
 
0.2%
30.3 1593
 
0.5%
30 3042
0.9%
29.1 1658
 
0.5%
28.8 1414
 
0.4%
28.7 1567
 
0.4%
28.6 1043
 
0.3%
27.9 2864
0.8%
27.8 4551
1.3%
27.7 4674
1.3%

feelslike
Real number (ℝ)

Distinct227
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.189142
Minimum-20.1
Maximum33.5
Zeros896
Zeros (%)0.3%
Negative34439
Negative (%)9.8%
Memory size2.7 MiB
2023-02-17T12:48:53.192659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-20.1
5-th percentile-2.3
Q17.4
median19.5
Q323.8
95-th percentile29.6
Maximum33.5
Range53.6
Interquartile range (IQR)16.4

Descriptive statistics

Standard deviation10.310905
Coefficient of variation (CV)0.6369025
Kurtosis-0.65784668
Mean16.189142
Median Absolute Deviation (MAD)6.6
Skewness-0.6041219
Sum5715333.6
Variance106.31476
MonotonicityNot monotonic
2023-02-17T12:48:53.503150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 7174
 
2.0%
23.3 7011
 
2.0%
23.5 6928
 
2.0%
17.1 6581
 
1.9%
22.2 5352
 
1.5%
24.1 4831
 
1.4%
20.8 4638
 
1.3%
24.5 4532
 
1.3%
23.1 4459
 
1.3%
26.7 4372
 
1.2%
Other values (217) 297157
84.2%
ValueCountFrequency (%)
-20.1 75
 
< 0.1%
-18.7 120
 
< 0.1%
-17.1 90
 
< 0.1%
-17 101
 
< 0.1%
-13 582
0.2%
-12.7 175
 
< 0.1%
-10.4 296
0.1%
-10.3 52
 
< 0.1%
-9.6 301
0.1%
-8.7 311
0.1%
ValueCountFrequency (%)
33.5 1593
0.5%
33 1600
0.5%
32.7 854
0.2%
32.4 1442
0.4%
31.3 1658
0.5%
30.8 1414
0.4%
30.6 1567
0.4%
30.3 1237
0.4%
30.1 1495
0.4%
30 1025
0.3%

precip
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct166
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5119011
Minimum0
Maximum72.817
Zeros200641
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:53.826992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.083
95-th percentile20.058
Maximum72.817
Range72.817
Interquartile range (IQR)2.083

Descriptive statistics

Standard deviation8.628572
Coefficient of variation (CV)2.4569519
Kurtosis20.813467
Mean3.5119011
Median Absolute Deviation (MAD)0
Skewness4.0030415
Sum1239824
Variance74.452254
MonotonicityNot monotonic
2023-02-17T12:48:54.108582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 200641
56.8%
0.002 4579
 
1.3%
0.295 1846
 
0.5%
16.289 1715
 
0.5%
0.297 1699
 
0.5%
1.174 1697
 
0.5%
15.43 1682
 
0.5%
0.014 1682
 
0.5%
1.095 1671
 
0.5%
20.842 1667
 
0.5%
Other values (156) 134156
38.0%
ValueCountFrequency (%)
0 200641
56.8%
0.002 4579
 
1.3%
0.004 807
 
0.2%
0.005 1626
 
0.5%
0.007 1620
 
0.5%
0.014 1682
 
0.5%
0.016 379
 
0.1%
0.018 1553
 
0.4%
0.038 1090
 
0.3%
0.054 726
 
0.2%
ValueCountFrequency (%)
72.817 467
 
0.1%
71.84 732
0.2%
56.579 1215
0.3%
49.725 130
 
< 0.1%
49.419 532
0.2%
41.371 457
 
0.1%
38.722 901
0.3%
35.679 147
 
< 0.1%
35.163 189
 
0.1%
34.533 1152
0.3%

dew
Real number (ℝ)

Distinct237
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8289507
Minimum-23.1
Maximum23.7
Zeros707
Zeros (%)0.2%
Negative77284
Negative (%)21.9%
Memory size2.7 MiB
2023-02-17T12:48:54.327344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-23.1
5-th percentile-8
Q11.7
median12.1
Q318.2
95-th percentile22.2
Maximum23.7
Range46.8
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation10.027654
Coefficient of variation (CV)1.0202161
Kurtosis-0.74225288
Mean9.8289507
Median Absolute Deviation (MAD)7.4
Skewness-0.58868835
Sum3469963.6
Variance100.55384
MonotonicityNot monotonic
2023-02-17T12:48:54.594306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 6000
 
1.7%
13.3 5682
 
1.6%
21.7 5199
 
1.5%
17.1 5187
 
1.5%
22.8 5138
 
1.5%
22.2 4699
 
1.3%
11.7 4517
 
1.3%
21.2 4348
 
1.2%
-0.4 4135
 
1.2%
19.5 4074
 
1.2%
Other values (227) 304056
86.1%
ValueCountFrequency (%)
-23.1 75
 
< 0.1%
-20.7 120
 
< 0.1%
-20.4 90
 
< 0.1%
-19.9 101
 
< 0.1%
-18 191
 
0.1%
-17.6 175
 
< 0.1%
-16.1 778
0.2%
-15.7 391
0.1%
-15.3 883
0.3%
-14.9 381
0.1%
ValueCountFrequency (%)
23.7 1237
 
0.4%
23 1212
 
0.3%
22.9 3092
0.9%
22.8 5138
1.5%
22.6 1179
 
0.3%
22.5 1512
 
0.4%
22.4 1343
 
0.4%
22.2 4699
1.3%
22.1 2815
0.8%
22 1308
 
0.4%

humidity
Real number (ℝ)

Distinct276
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.231963
Minimum22.6
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:54.845296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22.6
5-th percentile41.2
Q156
median66.8
Q377.6
95-th percentile89.2
Maximum96
Range73.4
Interquartile range (IQR)21.6

Descriptive statistics

Standard deviation14.814256
Coefficient of variation (CV)0.22367231
Kurtosis-0.57956187
Mean66.231963
Median Absolute Deviation (MAD)10.8
Skewness-0.19742869
Sum23382201
Variance219.46218
MonotonicityNot monotonic
2023-02-17T12:48:55.178177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.7 4472
 
1.3%
59.2 4432
 
1.3%
79.1 3988
 
1.1%
56.6 3755
 
1.1%
75.5 3625
 
1.0%
64.1 3593
 
1.0%
66.8 3544
 
1.0%
50.3 3435
 
1.0%
62.8 3353
 
0.9%
77.3 3233
 
0.9%
Other values (266) 315605
89.4%
ValueCountFrequency (%)
22.6 1274
0.4%
25.8 983
0.3%
28.7 381
 
0.1%
33 738
0.2%
34 771
0.2%
34.2 1219
0.3%
35.1 630
0.2%
36.1 966
0.3%
36.3 1109
0.3%
36.9 437
 
0.1%
ValueCountFrequency (%)
96 151
 
< 0.1%
95.8 1023
 
0.3%
95.1 375
 
0.1%
95 301
 
0.1%
94.4 1397
0.4%
94.1 457
 
0.1%
92.7 2720
0.8%
92.2 974
 
0.3%
91.7 1224
0.3%
91.5 2520
0.7%

snow
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075514609
Minimum0
Maximum12.5
Zeros345872
Zeros (%)98.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:55.498586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12.5
Range12.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.62919415
Coefficient of variation (CV)8.3320851
Kurtosis126.86002
Mean0.075514609
Median Absolute Deviation (MAD)0
Skewness10.452459
Sum26659.3
Variance0.39588528
MonotonicityNot monotonic
2023-02-17T12:48:55.925996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 345872
98.0%
1.9 1659
 
0.5%
3 650
 
0.2%
3.7 628
 
0.2%
1.5 562
 
0.2%
6.1 541
 
0.2%
8.1 492
 
0.1%
1.2 485
 
0.1%
5 476
 
0.1%
1 469
 
0.1%
Other values (6) 1201
 
0.3%
ValueCountFrequency (%)
0 345872
98.0%
1 469
 
0.1%
1.2 485
 
0.1%
1.5 562
 
0.2%
1.9 1659
 
0.5%
3 650
 
0.2%
3.7 628
 
0.2%
4.9 396
 
0.1%
5 476
 
0.1%
5.7 120
 
< 0.1%
ValueCountFrequency (%)
12.5 91
 
< 0.1%
10.6 52
 
< 0.1%
8.1 492
0.1%
7.5 128
 
< 0.1%
6.1 541
0.2%
5.8 414
0.1%
5.7 120
 
< 0.1%
5 476
0.1%
4.9 396
0.1%
3.7 628
0.2%

snowdepth
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15625306
Minimum0
Maximum16.4
Zeros336604
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:56.135558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum16.4
Range16.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1091937
Coefficient of variation (CV)7.0987004
Kurtosis96.394366
Mean0.15625306
Median Absolute Deviation (MAD)0
Skewness9.2708642
Sum55162.8
Variance1.2303106
MonotonicityNot monotonic
2023-02-17T12:48:56.366516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 336604
95.3%
0.1 2570
 
0.7%
0.6 1627
 
0.5%
1.8 1047
 
0.3%
0.8 1041
 
0.3%
0.5 762
 
0.2%
3.4 691
 
0.2%
4.3 632
 
0.2%
8.3 628
 
0.2%
0.2 616
 
0.2%
Other values (19) 6817
 
1.9%
ValueCountFrequency (%)
0 336604
95.3%
0.1 2570
 
0.7%
0.2 616
 
0.2%
0.3 560
 
0.2%
0.5 762
 
0.2%
0.6 1627
 
0.5%
0.8 1041
 
0.3%
1.2 384
 
0.1%
1.7 476
 
0.1%
1.8 1047
 
0.3%
ValueCountFrequency (%)
16.4 120
 
< 0.1%
15.2 203
 
0.1%
14.6 90
 
< 0.1%
13.1 322
0.1%
12.3 414
0.1%
9.7 509
0.1%
9.5 396
0.1%
8.3 628
0.2%
6.8 304
0.1%
6.4 579
0.2%

windspeed
Real number (ℝ)

Distinct162
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.475578
Minimum5.6
Maximum49.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:56.566956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.6
5-th percentile9.4
Q114.7
median19.9
Q325.3
95-th percentile36
Maximum49.5
Range43.9
Interquartile range (IQR)10.6

Descriptive statistics

Standard deviation8.181812
Coefficient of variation (CV)0.39958882
Kurtosis0.31816553
Mean20.475578
Median Absolute Deviation (MAD)5.2
Skewness0.66677106
Sum7228595.7
Variance66.942047
MonotonicityNot monotonic
2023-02-17T12:48:56.789666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.3 10061
 
2.8%
22.6 9385
 
2.7%
17.9 8511
 
2.4%
18.4 8475
 
2.4%
9.4 7950
 
2.3%
7.8 6524
 
1.8%
13 6518
 
1.8%
17.4 6358
 
1.8%
15.6 6132
 
1.7%
21.9 6008
 
1.7%
Other values (152) 277113
78.5%
ValueCountFrequency (%)
5.6 1238
 
0.4%
5.7 1751
 
0.5%
7.6 2529
 
0.7%
7.7 2259
 
0.6%
7.8 6524
1.8%
7.9 1610
 
0.5%
9.3 1414
 
0.4%
9.4 7950
2.3%
9.5 4362
1.2%
9.6 3092
 
0.9%
ValueCountFrequency (%)
49.5 52
 
< 0.1%
48.9 812
0.2%
47 205
 
0.1%
45.4 477
 
0.1%
44.3 494
 
0.1%
44 722
0.2%
43.5 607
 
0.2%
43.1 1070
0.3%
42.5 1048
0.3%
42.1 1706
0.5%

visibility
Real number (ℝ)

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.495764
Minimum4.2
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:57.183900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.2
5-th percentile9.7
Q113.6
median15.6
Q316
95-th percentile16
Maximum16
Range11.8
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation2.1149088
Coefficient of variation (CV)0.14589841
Kurtosis2.3367026
Mean14.495764
Median Absolute Deviation (MAD)0.4
Skewness-1.6745384
Sum5117511.9
Variance4.4728394
MonotonicityNot monotonic
2023-02-17T12:48:57.499534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 136676
38.7%
15.8 12285
 
3.5%
15.9 10999
 
3.1%
15.6 10782
 
3.1%
15.5 7688
 
2.2%
13.5 7626
 
2.2%
14.6 7623
 
2.2%
15.3 7475
 
2.1%
14.4 7150
 
2.0%
15.4 6644
 
1.9%
Other values (65) 138087
39.1%
ValueCountFrequency (%)
4.2 301
 
0.1%
5.6 91
 
< 0.1%
6.4 151
 
< 0.1%
7.1 2005
0.6%
7.2 370
 
0.1%
7.3 147
 
< 0.1%
7.5 662
 
0.2%
7.6 2206
0.6%
7.7 250
 
0.1%
7.8 940
0.3%
ValueCountFrequency (%)
16 136676
38.7%
15.9 10999
 
3.1%
15.8 12285
 
3.5%
15.7 6390
 
1.8%
15.6 10782
 
3.1%
15.5 7688
 
2.2%
15.4 6644
 
1.9%
15.3 7475
 
2.1%
15.2 2144
 
0.6%
15.1 4850
 
1.4%

solarradiation
Real number (ℝ)

Distinct345
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.47613
Minimum11
Maximum331.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:57.726922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile33.2
Q184.1
median162.5
Q3242.8
95-th percentile315.6
Maximum331.4
Range320.4
Interquartile range (IQR)158.7

Descriptive statistics

Standard deviation90.765205
Coefficient of variation (CV)0.54195904
Kurtosis-1.2138182
Mean167.47613
Median Absolute Deviation (MAD)80.1
Skewness0.1233206
Sum59124937
Variance8238.3224
MonotonicityNot monotonic
2023-02-17T12:48:57.911708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
208.7 3060
 
0.9%
242.6 2984
 
0.8%
268.7 2664
 
0.8%
273.5 2577
 
0.7%
52.5 2455
 
0.7%
77.5 2257
 
0.6%
102.7 1946
 
0.6%
81.5 1820
 
0.5%
315.6 1788
 
0.5%
232.4 1753
 
0.5%
Other values (335) 329731
93.4%
ValueCountFrequency (%)
11 375
 
0.1%
11.1 52
 
< 0.1%
12.4 250
 
0.1%
12.5 189
 
0.1%
13.6 313
 
0.1%
14.1 1023
0.3%
14.5 130
 
< 0.1%
15.2 457
0.1%
17.1 147
 
< 0.1%
19 872
0.2%
ValueCountFrequency (%)
331.4 1113
0.3%
327.5 1147
0.3%
327.2 1616
0.5%
326 1598
0.5%
325.2 1150
0.3%
323.9 1477
0.4%
321.9 1549
0.4%
319.2 1653
0.5%
317.6 1043
0.3%
317.4 1219
0.3%

cloudcover
Real number (ℝ)

Distinct276
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.742566
Minimum0.1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2023-02-17T12:48:58.269387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q117.3
median43.7
Q374
95-th percentile99.5
Maximum100
Range99.9
Interquartile range (IQR)56.7

Descriptive statistics

Standard deviation32.567541
Coefficient of variation (CV)0.71197452
Kurtosis-1.2347865
Mean45.742566
Median Absolute Deviation (MAD)28.1
Skewness0.19209673
Sum16148727
Variance1060.6447
MonotonicityNot monotonic
2023-02-17T12:48:58.689206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 14230
 
4.0%
0.7 5799
 
1.6%
0.5 5015
 
1.4%
0.4 4420
 
1.3%
0.9 4320
 
1.2%
66.8 4093
 
1.2%
99.5 4077
 
1.2%
65.4 4055
 
1.1%
0.6 3805
 
1.1%
66.4 3142
 
0.9%
Other values (266) 300079
85.0%
ValueCountFrequency (%)
0.1 381
 
0.1%
0.2 2511
0.7%
0.3 2156
 
0.6%
0.4 4420
1.3%
0.5 5015
1.4%
0.6 3805
1.1%
0.7 5799
1.6%
0.9 4320
1.2%
1 1934
 
0.5%
1.1 2776
0.8%
ValueCountFrequency (%)
100 14230
4.0%
99.9 1012
 
0.3%
99.8 1212
 
0.3%
99.6 250
 
0.1%
99.5 4077
 
1.2%
99.1 1928
 
0.5%
98.7 379
 
0.1%
98.1 1631
 
0.5%
97.9 1231
 
0.3%
97.8 1224
 
0.3%

conditions
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
cloudy_rain
254748 
Clear
91552 
snow_rain
 
6735

Length

Max length11
Median length11
Mean length9.4058748
Min length5

Characters and Unicode

Total characters3320603
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear
2nd rowClear
3rd rowClear
4th rowClear
5th rowClear

Common Values

ValueCountFrequency (%)
cloudy_rain 254748
72.2%
Clear 91552
 
25.9%
snow_rain 6735
 
1.9%

Length

2023-02-17T12:48:58.984262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T12:48:59.163462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
cloudy_rain 254748
72.2%
clear 91552
 
25.9%
snow_rain 6735
 
1.9%

Most occurring characters

ValueCountFrequency (%)
r 353035
10.6%
a 353035
10.6%
l 346300
10.4%
n 268218
8.1%
o 261483
7.9%
_ 261483
7.9%
i 261483
7.9%
c 254748
7.7%
u 254748
7.7%
d 254748
7.7%
Other values (5) 451322
13.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2967568
89.4%
Connector Punctuation 261483
 
7.9%
Uppercase Letter 91552
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 353035
11.9%
a 353035
11.9%
l 346300
11.7%
n 268218
9.0%
o 261483
8.8%
i 261483
8.8%
c 254748
8.6%
u 254748
8.6%
d 254748
8.6%
y 254748
8.6%
Other values (3) 105022
 
3.5%
Connector Punctuation
ValueCountFrequency (%)
_ 261483
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 91552
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3059120
92.1%
Common 261483
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 353035
11.5%
a 353035
11.5%
l 346300
11.3%
n 268218
8.8%
o 261483
8.5%
i 261483
8.5%
c 254748
8.3%
u 254748
8.3%
d 254748
8.3%
y 254748
8.3%
Other values (4) 196574
6.4%
Common
ValueCountFrequency (%)
_ 261483
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3320603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 353035
10.6%
a 353035
10.6%
l 346300
10.4%
n 268218
8.1%
o 261483
7.9%
_ 261483
7.9%
i 261483
7.9%
c 254748
7.7%
u 254748
7.7%
d 254748
7.7%
Other values (5) 451322
13.6%

description
Categorical

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Clear conditions throughout the day.
91552 
Partly cloudy throughout the day.
65086 
Becoming cloudy in the afternoon.
22155 
Partly cloudy throughout the day with late afternoon rain.
18353 
Partly cloudy throughout the day with rain.
16268 
Other values (39)
139621 

Length

Max length82
Median length81
Mean length43.951506
Min length26

Characters and Unicode

Total characters15516420
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear conditions throughout the day.
2nd rowClear conditions throughout the day.
3rd rowClear conditions throughout the day.
4th rowClear conditions throughout the day.
5th rowClear conditions throughout the day.

Common Values

ValueCountFrequency (%)
Clear conditions throughout the day. 91552
25.9%
Partly cloudy throughout the day. 65086
18.4%
Becoming cloudy in the afternoon. 22155
 
6.3%
Partly cloudy throughout the day with late afternoon rain. 18353
 
5.2%
Partly cloudy throughout the day with rain. 16268
 
4.6%
Partly cloudy throughout the day with rain clearing later. 13963
 
4.0%
Cloudy skies throughout the day. 12706
 
3.6%
Cloudy skies throughout the day with a chance of rain throughout the day. 11247
 
3.2%
Partly cloudy throughout the day with a chance of rain throughout the day. 9267
 
2.6%
Clearing in the afternoon. 9142
 
2.6%
Other values (34) 83296
23.6%

Length

2023-02-17T12:48:59.429875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 382040
15.9%
throughout 316485
13.2%
day 316485
13.2%
cloudy 235460
9.8%
with 152394
 
6.3%
rain 152147
 
6.3%
partly 149674
 
6.2%
afternoon 108961
 
4.5%
conditions 97515
 
4.1%
clear 97515
 
4.1%
Other values (14) 396686
16.5%

Most occurring characters

ValueCountFrequency (%)
2052327
13.2%
t 1578125
 
10.2%
o 1397362
 
9.0%
h 1190906
 
7.7%
a 994139
 
6.4%
r 949782
 
6.1%
u 868430
 
5.6%
n 820818
 
5.3%
e 814654
 
5.3%
i 726643
 
4.7%
Other values (14) 4123234
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12758023
82.2%
Space Separator 2052327
 
13.2%
Other Punctuation 353035
 
2.3%
Uppercase Letter 353035
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1578125
12.4%
o 1397362
11.0%
h 1190906
9.3%
a 994139
 
7.8%
r 949782
 
7.4%
u 868430
 
6.8%
n 820818
 
6.4%
e 814654
 
6.4%
i 726643
 
5.7%
y 726306
 
5.7%
Other values (9) 2690858
21.1%
Uppercase Letter
ValueCountFrequency (%)
C 163369
46.3%
P 149674
42.4%
B 39992
 
11.3%
Space Separator
ValueCountFrequency (%)
2052327
100.0%
Other Punctuation
ValueCountFrequency (%)
. 353035
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13111058
84.5%
Common 2405362
 
15.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1578125
12.0%
o 1397362
10.7%
h 1190906
 
9.1%
a 994139
 
7.6%
r 949782
 
7.2%
u 868430
 
6.6%
n 820818
 
6.3%
e 814654
 
6.2%
i 726643
 
5.5%
y 726306
 
5.5%
Other values (12) 3043893
23.2%
Common
ValueCountFrequency (%)
2052327
85.3%
. 353035
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15516420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2052327
13.2%
t 1578125
 
10.2%
o 1397362
 
9.0%
h 1190906
 
7.7%
a 994139
 
6.4%
r 949782
 
6.1%
u 868430
 
5.6%
n 820818
 
5.3%
e 814654
 
5.3%
i 726643
 
4.7%
Other values (14) 4123234
26.6%

seasons
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
summer
127316 
autumn
102839 
spring
74990 
winter
47890 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2118210
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwinter
2nd rowwinter
3rd rowwinter
4th rowwinter
5th rowwinter

Common Values

ValueCountFrequency (%)
summer 127316
36.1%
autumn 102839
29.1%
spring 74990
21.2%
winter 47890
 
13.6%

Length

2023-02-17T12:48:59.663064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T12:49:00.060138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
summer 127316
36.1%
autumn 102839
29.1%
spring 74990
21.2%
winter 47890
 
13.6%

Most occurring characters

ValueCountFrequency (%)
m 357471
16.9%
u 332994
15.7%
r 250196
11.8%
n 225719
10.7%
s 202306
9.6%
e 175206
8.3%
t 150729
7.1%
i 122880
 
5.8%
a 102839
 
4.9%
p 74990
 
3.5%
Other values (2) 122880
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2118210
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 357471
16.9%
u 332994
15.7%
r 250196
11.8%
n 225719
10.7%
s 202306
9.6%
e 175206
8.3%
t 150729
7.1%
i 122880
 
5.8%
a 102839
 
4.9%
p 74990
 
3.5%
Other values (2) 122880
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2118210
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 357471
16.9%
u 332994
15.7%
r 250196
11.8%
n 225719
10.7%
s 202306
9.6%
e 175206
8.3%
t 150729
7.1%
i 122880
 
5.8%
a 102839
 
4.9%
p 74990
 
3.5%
Other values (2) 122880
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2118210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 357471
16.9%
u 332994
15.7%
r 250196
11.8%
n 225719
10.7%
s 202306
9.6%
e 175206
8.3%
t 150729
7.1%
i 122880
 
5.8%
a 102839
 
4.9%
p 74990
 
3.5%
Other values (2) 122880
 
5.8%

Interactions

2023-02-17T12:48:25.163326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:00.778593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:11.207097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:21.978647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:32.153189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:42.589363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:53.416884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:03.686256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:14.588408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:25.261468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:38.496262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:57.585256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:08.326746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:18.423049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:28.905592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:38.747514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:47.865319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:57.377820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:07.356673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:16.874967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:26.557838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:37.021886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:45.898443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:55.235657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:48:05.076037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:48:14.891603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:48:25.473416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:01.273402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:11.644241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:22.343268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:32.564031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:42.950414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:53.846953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:04.105235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:14.975965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-17T12:45:37.729092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:56.835131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:07.518107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:17.650048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:28.127739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:38.037127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:47.236847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:56.594488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:06.366611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:16.171990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:25.786145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:36.384670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:44.988124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:54.433474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:48:04.479388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:48:14.202959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:48:24.403907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:48:34.791083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:10.881001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:21.608467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:31.804935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:42.160480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:44:53.017169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:03.370553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:14.223657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:24.555618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:38.070635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:45:57.217610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:07.917000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:18.053741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:28.520853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:38.396526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:47.541371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:46:57.006748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:06.977738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:16.511508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:26.177865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:36.725147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:45.348545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:47:54.798102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:48:04.769924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:48:14.556556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-17T12:48:24.838690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-17T12:49:00.413748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2023-02-17T12:49:01.259902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-17T12:49:02.187531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-17T12:49:02.697917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-17T12:49:03.139951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-02-17T12:49:03.625075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-02-17T12:48:35.726257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-17T12:48:38.853721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

tripdurationstart_station_idstart_latstart_lonend_station_idend_latend_lonbikeidusertypegenderdistmonthdayhourminbirthyearyears_oldholidaytempmaxtempmintempfeelslikeprecipdewhumiditysnowsnowdepthwindspeedvisibilitysolarradiationcloudcoverconditionsdescriptionseasons
0932318340.716247-74.033459319940.728745-74.03210831929Subscribermale1.084267Januaryweekday26199226holiday-7.8-13.8-10.7-17.00.0-19.947.80.00.118.516.0106.70.3ClearClear conditions throughout the day.winter
1550318340.716247-74.033459319940.728745-74.03210831845Subscriberfemale1.084267Januaryweekday126196949holiday-7.8-13.8-10.7-17.00.0-19.947.80.00.118.516.0106.70.3ClearClear conditions throughout the day.winter
2510318340.716247-74.033459319940.728745-74.03210831708Subscribermale1.084267Januaryweekday126194672holiday-7.8-13.8-10.7-17.00.0-19.947.80.00.118.516.0106.70.3ClearClear conditions throughout the day.winter
3354318340.716247-74.033459326740.712419-74.03852631697Subscribermale0.415696Januaryweekday1453199424holiday-7.8-13.8-10.7-17.00.0-19.947.80.00.118.516.0106.70.3ClearClear conditions throughout the day.winter
4250318340.716247-74.033459363940.719252-74.03423431861Subscribermale0.240932Januaryweekday1734199127holiday-7.8-13.8-10.7-17.00.0-19.947.80.00.118.516.0106.70.3ClearClear conditions throughout the day.winter
5613318340.716247-74.033459320340.727596-74.04424731859Subscribermale1.217917Januaryweekday225198236holiday-7.8-13.8-10.7-17.00.0-19.947.80.00.118.516.0106.70.3ClearClear conditions throughout the day.winter
6290318340.716247-74.033459326740.712419-74.03852631694Subscribermale0.415696Januaryweekday1213195860working_day-3.3-10.9-7.5-13.00.0-15.752.20.00.024.316.0104.44.3ClearClear conditions throughout the day.winter
7381318340.716247-74.033459320540.716540-74.04963831754Subscriberfemale1.324928Januaryweekday1250198929working_day-3.3-10.9-7.5-13.00.0-15.752.20.00.024.316.0104.44.3ClearClear conditions throughout the day.winter
8318318340.716247-74.033459327540.718355-74.03891431816Subscribermale0.507020Januaryweekday1355196058working_day-3.3-10.9-7.5-13.00.0-15.752.20.00.024.316.0104.44.3ClearClear conditions throughout the day.winter
91852318340.716247-74.033459328140.745910-74.05727131754Subscribermale2.826499Januaryweekday1655197642working_day-3.3-10.9-7.5-13.00.0-15.752.20.00.024.316.0104.44.3ClearClear conditions throughout the day.winter
tripdurationstart_station_idstart_latstart_lonend_station_idend_latend_lonbikeidusertypegenderdistmonthdayhourminbirthyearyears_oldholidaytempmaxtempmintempfeelslikeprecipdewhumiditysnowsnowdepthwindspeedvisibilitysolarradiationcloudcoverconditionsdescriptionseasons
353025667369440.71113-74.0789319540.730743-74.06378429299Subscribermale1.934962Decemberweekday1139199424working_day5.7-3.31.1-2.20.000-7.752.50.00.021.316.097.00.9ClearClear conditions throughout the day.winter
353026687369440.71113-74.0789319540.730743-74.06378429258Subscribermale1.934962Decemberweekday1328199424working_day8.33.35.83.85.1191.575.10.00.021.311.946.443.4cloudy_rainBecoming cloudy in the afternoon with rain.winter
353027625369440.71113-74.0789319540.730743-74.06378429664Subscribermale1.934962Decemberweekday125199424working_day16.28.313.813.741.37112.894.10.00.037.57.515.293.4cloudy_rainCloudy skies throughout the day with a chance of rain throughout the day.winter
353028826369440.71113-74.0789318640.719586-74.04311726306Subscribermale2.657139Decemberweekday1634199127working_day16.28.313.813.741.37112.894.10.00.037.57.515.293.4cloudy_rainCloudy skies throughout the day with a chance of rain throughout the day.winter
353029640369440.71113-74.0789319540.730743-74.06378426298Subscribermale1.934962Decemberweekend1027199424working_day11.65.07.74.20.5881.867.40.00.045.415.347.890.7cloudy_rainCloudy skies throughout the day with early morning rain.winter
3530301081369440.71113-74.0789326940.726012-74.05038929586Subscribermale1.872980Decemberweekend1151199325working_day11.65.07.74.20.5881.867.40.00.045.415.347.890.7cloudy_rainCloudy skies throughout the day with early morning rain.winter
353031344369440.71113-74.0789328040.719282-74.07126226241Subscriberfemale0.828647Decemberweekday2140198335holiday4.41.22.4-2.00.000-6.452.80.00.024.916.086.533.7cloudy_rainPartly cloudy throughout the day.winter
3530321233369440.71113-74.0789318640.719586-74.04311729294Subscribermale2.657139Decemberweekend1255198830working_day13.84.39.57.40.0002.763.90.00.039.215.894.264.6cloudy_rainPartly cloudy throughout the day.winter
3530331057369440.71113-74.0789321340.718489-74.04772729475Subscriberfemale2.315132Decemberweekend1532199127working_day3.91.12.70.00.000-3.166.00.00.020.815.535.673.2cloudy_rainPartly cloudy throughout the day.winter
353034301369440.71113-74.0789327740.714358-74.06661126270Subscribermale0.902881Decemberweekday1634199127working_day7.82.15.53.521.7561.978.20.00.021.712.434.874.0cloudy_rainPartly cloudy throughout the day with rain.winter